CHATBOTS

Receipt-drop reimbursement simulator

When an employee drops a receipt image into a Slack channel, the workflow extracts the amount, vendor, and category, simulates the reimbursement against policy.

CategoryChatbots
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerReceipt image posted to Slack channelSlack
  • ActionOCR and extract line items with the modelOpenAI
  • ActionLoad caps and exclusion rules from AirtableAirtableAirtable
  • LogicNet reimbursable vs excluded line items
  • OutputPost expected payout and deduction breakdownSlack

What it does

Turns a photo of a receipt into a pre-submission verdict. An employee uploads a receipt to a designated Slack channel; the workflow reads the image, pulls out vendor, total, date, and line items, then runs each line against the expense policy to show exactly how much will be reimbursed and which items (alcohol, upgrades, personal add-ons) get deducted.

When to use it

Use it for travel- and meal-heavy teams where the gap between receipt total and reimbursable total causes friction. Employees see the deduction logic up front instead of discovering it on their paycheck.

How it works

  1. 1A receipt image is posted to a watched Slack channel, firing the trigger.
  2. 2The model performs OCR and structured extraction on the image to capture vendor, total, date, and line items.
  3. 3Policy caps and exclusion rules are loaded from Airtable.
  4. 4A logic step nets reimbursable line items against excluded ones to compute the expected payout.
  5. 5The bot replies with the reimbursable total, a deduction breakdown, and a flag if pre-approval is still required.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SlackChannels, DMs, threads, mentions.
  2. 2
    Connect AirtableBases, tables, views, automations.
  3. 3
    Connect OpenAIModels, embeddings, files.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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